Erfan Shayegani - Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness

00:00 Intro and Paper Overview 00:46 What Are Computer Use Agents 02:50 Safety Focus and Blind Goal Directedness 07:37 Pattern One Context Failures 13:45 Pattern Two Risky Assumptions 17:47 Pattern Three Infeasible Goals 20:02 BlindAct Benchmark Setup 23:45 Evaluation With LLM Judges 25:20 Results BGD vs Completion 31:41 Prompting Mitigations Limits 35:03 Qualitative Failure Modes 39:51 Q&A and Closing In this talk Erfan will talk about adversarial attacks on multi-modal language models. Computer-Use Agents (CUAs) are an increasingly deployed class of agents that take actions on GUIs to accomplish user goals. In this paper, we show that CUAs consistently exhibit Blind Goal-Directedness (BGD): a bias to pursue goals regardless of feasibility, safety, reliability, or context. We characterize three prevalent patterns of BGD: (i) lack of contextual reasoning, (ii) assumptions and decisions under ambiguity, and (iii) contradictory or infeasible goals. We develop BLIND-ACT, a benchmark of 90 tasks capturing these three patterns. Built on OSWorld, BLIND-ACT provides realistic environments and employs LLM-based judges to evaluate agent behavior, achieving 93.75% agreement with human annotations. We use BLIND-ACT to evaluate nine frontier models, including Claude Sonnet and Opus 4, Computer-Use-Preview, and GPT-5, observing high average BGD rates (80.8%) across them. We show that BGD exposes subtle risks that arise even when inputs are not directly harmful. While prompting-based interventions lower BGD levels, substantial risk persists, highlighting the need for stronger training- or inference-time interventions. Qualitative analysis reveals observed failure modes: execution-first bias (focusing on how to act over whether to act), thought–action disconnect (execution diverging from reasoning), and request-primacy (justifying actions due to user request). Identifying BGD and introducing BLIND-ACT establishes a foundation for future research on studying and mitigating this f